An adaptive equalizer based receiver, such as a normalized least mean square (NLMS)-based receiver, provides superior performance for high data rate services such as frequency division duplex (FDD) high speed downlink packet access (HSDPA) or code division multiple access (CDMA) 2000 evolution data voice (EV-DV) over a Rake receiver. A typical NLMS receiver includes an adaptive equalizer having an equalizer filter and a tap coefficients generator to generate the tap coefficients used to update the filter coefficients of the equalizer filter. The equalizer filter is typically a finite impulse response (FIR) filter.
An adaptive step-size parameter, μ, (“mu”), in an adaptive equalization algorithm controls the rate of convergence of the equalizer filter. The adaptation step-size parameter μ is a critical parameter that impacts the performance of the adaptive equalizer. The adaptive step-size parameter μ is typically defined prior to operation of the equalizer filter or varied in a deterministic way. The step-size is the size of each step in an iterative (loop) algorithm that attempts to converge to some point, such as least mean square (LMS), NLMS or its derivatives. Large step-sizes help the adaptive equalizer converge (in as accurate a manner as is possible) in a short period of time, but the adaptive equalizer would converge more accurately if the step-size was smaller. Thus, there is a trade-off between quick and accurate convergence. The ideal balance between convergence speed and accuracy depends on how fast the point on which the algorithm is trying to converge to is changing. The convergence time is inversely related to the adaptation step-size parameter μ. Therefore, with a larger step-size, the convergence may be obtained quickly.
However, the large step-size may cause misadjustment errors which impact the raw bit error rate (BER) performance of the adaptive equalizer. The misadjustment errors are due to the convergence of the LMS never being fully achieved because the step size used is approximately the closest each point on the vector may come to the desired point.